Minimum Variance Prediction for Linear Time-Varying Systems

Zheng Li; R J Evans; Björn Wittenmark

doi:10.1016/S1474-6670(17)47783-4

Minimum Variance Prediction for Linear Time-Varying Systems

Zheng Li, R J Evans, Björn Wittenmark

Department of Automatic Control

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

In this paper we study the problem of minimum variance prediction for linear time-varying systems. We consider the standard time-varying autoregression moving average (ARMA) model and develop a predictor which guarantees minimum variance prediction for a large class of linear time-varying systems. The predictor is developed based on a pseudocommutation technique for dealing with noncommutativity of linear time-varying operators in a transfer operator framework. We also show connections between this input-output predictor and the Kalman predictor via an example.

Original language	English
Title of host publication	IFAC Proceedings Volumes
Volume	27:8
DOIs	https://doi.org/10.1016/S1474-6670(17)47783-4
Publication status	Published - 1994
Event	10th IFAC Symposium on System Identification, SYSID'94 - Copenhagen, Denmark Duration: 1994 Jul 4 → …

Conference

Conference	10th IFAC Symposium on System Identification, SYSID'94
Country/Territory	Denmark
City	Copenhagen
Period	1994/07/04 → …

Subject classification (UKÄ)

Control Engineering

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10.1016/S1474-6670(17)47783-4

Cite this

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title = "Minimum Variance Prediction for Linear Time-Varying Systems",

abstract = "In this paper we study the problem of minimum variance prediction for linear time-varying systems. We consider the standard time-varying autoregression moving average (ARMA) model and develop a predictor which guarantees minimum variance prediction for a large class of linear time-varying systems. The predictor is developed based on a pseudocommutation technique for dealing with noncommutativity of linear time-varying operators in a transfer operator framework. We also show connections between this input-output predictor and the Kalman predictor via an example.",

author = "Zheng Li and Evans, \{R J\} and Bj{\"o}rn Wittenmark",

year = "1994",

doi = "10.1016/S1474-6670(17)47783-4",

language = "English",

volume = "27:8",

booktitle = "IFAC Proceedings Volumes",

note = "10th IFAC Symposium on System Identification, SYSID'94 ; Conference date: 04-07-1994",

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AU - Li, Zheng

AU - Evans, R J

AU - Wittenmark, Björn

PY - 1994

Y1 - 1994

N2 - In this paper we study the problem of minimum variance prediction for linear time-varying systems. We consider the standard time-varying autoregression moving average (ARMA) model and develop a predictor which guarantees minimum variance prediction for a large class of linear time-varying systems. The predictor is developed based on a pseudocommutation technique for dealing with noncommutativity of linear time-varying operators in a transfer operator framework. We also show connections between this input-output predictor and the Kalman predictor via an example.

AB - In this paper we study the problem of minimum variance prediction for linear time-varying systems. We consider the standard time-varying autoregression moving average (ARMA) model and develop a predictor which guarantees minimum variance prediction for a large class of linear time-varying systems. The predictor is developed based on a pseudocommutation technique for dealing with noncommutativity of linear time-varying operators in a transfer operator framework. We also show connections between this input-output predictor and the Kalman predictor via an example.

U2 - 10.1016/S1474-6670(17)47783-4

DO - 10.1016/S1474-6670(17)47783-4

M3 - Paper in conference proceeding

VL - 27:8

BT - IFAC Proceedings Volumes

T2 - 10th IFAC Symposium on System Identification, SYSID'94

Y2 - 4 July 1994

ER -

Minimum Variance Prediction for Linear Time-Varying Systems

Abstract

Conference

Subject classification (UKÄ)

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